Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	136,478
2	South Dakota	134,153
3	Rhode Island	131,729
4	Utah	120,985
5	Tennessee	117,717
6	Arizona	116,158
7	Iowa	112,376
8	Oklahoma	111,232
9	Wisconsin	110,076
10	Nebraska	109,869
11	Arkansas	109,768
12	South Carolina	108,338
13	Alabama	105,464
14	New Jersey	105,124
15	Kansas	104,866
16	Indiana	103,313
17	Mississippi	102,932
18	Idaho	102,019
19	Illinois	99,852
20	Delaware	99,414
21	Nevada	99,237
22	Montana	98,682
23	New York	98,540
24	Georgia	98,215
25	Wyoming	97,828
26	Florida	97,349
27	Kentucky	96,978
28	Texas	96,969
29	Louisiana	96,144
30	Missouri	95,899
31	Minnesota	94,108
32	Massachusetts	94,018
33	California	93,266
34	New Mexico	91,935
35	Connecticut	89,117
36	North Carolina	88,603
37	Ohio	88,013
38	Alaska	86,892
39	Pennsylvania	82,447
40	Colorado	82,125
41	West Virginia	80,356
42	Michigan	78,673
43	Virginia	73,528
44	Maryland	69,345
45	District of Columbia	64,093
46	New Hampshire	63,623
47	Washington	49,012
48	Puerto Rico	45,041
49	Oregon	39,757
50	Maine	38,889
51	Vermont	32,649
52	Hawaii	21,526

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Michigan	580
2	Rhode Island	407
3	New Jersey	388
4	Connecticut	352
5	New York	350
6	Pennsylvania	307
7	Alaska	263
8	New Hampshire	260
9	Minnesota	259
10	Vermont	250
11	Delaware	249
12	North Carolina	233
13	Massachusetts	231
14	Colorado	225
15	Tennessee	217
16	Florida	214
17	Illinois	202
18	Maine	200
19	Idaho	187
20	Maryland	187
21	North Dakota	173
22	West Virginia	168
23	South Carolina	163
24	Virginia	145
25	District of Columbia	142
26	Iowa	142
27	Nebraska	138
28	Ohio	136
29	Washington	125
30	Wisconsin	122
31	South Dakota	120
32	Montana	117
33	Texas	116
34	Indiana	112
35	Louisiana	106
36	Georgia	99
37	Wyoming	99
38	New Mexico	98
39	Oregon	95
40	Puerto Rico	86
41	Utah	84
42	Oklahoma	83
43	Nevada	79
44	Missouri	77
45	Arizona	75
46	Kentucky	73
47	California	58
48	Hawaii	54
49	Kansas	42
50	Mississippi	39
51	Alabama	33
52	Arkansas	32

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	2,780
2	New York	2,582
3	Massachusetts	2,515
4	Rhode Island	2,484
5	Mississippi	2,376
6	Arizona	2,335
7	Connecticut	2,224
8	South Dakota	2,190
9	Louisiana	2,188
10	Alabama	2,172
11	Pennsylvania	1,975
12	North Dakota	1,967
13	Indiana	1,943
14	New Mexico	1,889
15	Arkansas	1,873
16	Illinois	1,868
17	Iowa	1,849
18	South Carolina	1,787
19	Georgia	1,756
20	Tennessee	1,737
21	Michigan	1,735
22	Nevada	1,719
23	Kansas	1,691
24	Texas	1,688
25	Delaware	1,609
26	Ohio	1,603
27	Florida	1,572
28	District of Columbia	1,521
29	California	1,514
30	West Virginia	1,510
31	Missouri	1,466
32	Kentucky	1,405
33	Maryland	1,385
34	Montana	1,381
35	Wisconsin	1,262
36	Oklahoma	1,251
37	Minnesota	1,235
38	Virginia	1,218
39	Wyoming	1,211
40	Nebraska	1,183
41	North Carolina	1,164
42	Idaho	1,106
43	Colorado	1,075
44	New Hampshire	918
45	Washington	702
46	Puerto Rico	669
47	Utah	666
48	Oregon	576
49	Maine	554
50	Alaska	408
51	Vermont	366
52	Hawaii	327

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Kentucky	5
2	Virginia	4
3	Montana	3
4	New York	3
5	Connecticut	2
6	Massachusetts	2
7	Michigan	2
8	Mississippi	2
9	Nevada	2
10	New Jersey	2
11	Ohio	2
12	Oregon	2
13	Tennessee	2
14	West Virginia	2
15	Alabama	1
16	Arkansas	1
17	California	1
18	District of Columbia	1
19	Florida	1
20	Idaho	1
21	Iowa	1
22	Maryland	1
23	New Mexico	1
24	North Carolina	1
25	Pennsylvania	1
26	Puerto Rico	1
27	Texas	1
28	Washington	1
29	Alaska	0
30	Arizona	0
31	Colorado	0
32	Delaware	0
33	Georgia	0
34	Hawaii	0
35	Illinois	0
36	Indiana	0
37	Kansas	0
38	Louisiana	0
39	Maine	0
40	Minnesota	0
41	Missouri	0
42	Nebraska	0
43	New Hampshire	0
44	North Dakota	0
45	Oklahoma	0
46	Rhode Island	0
47	South Carolina	0
48	South Dakota	0
49	Utah	0
50	Vermont	0
51	Wisconsin	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Crowley	Colorado	353,902	1	99
Chattahoochee	Georgia	331,530	2	99
Bent	Colorado	266,272	3	99
Dewey	South Dakota	244,569	4	99
Lincoln	Arkansas	244,395	5	99
Davidson	Tennessee	137,153	194	93
Richland	South Carolina	106,482	1018	67
York	South Carolina	103,275	1165	62
Orange	California	84,121	2061	34
Pierce	Washington	48,236	2900	7

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Foard	Texas	8,658	1	99
Gove	Kansas	8,346	2	99
Jerauld	South Dakota	7,948	3	99
Galax city	Virginia	7,878	4	99
Emporia city	Virginia	7,856	5	99
Orange	California	1,506	1764	43
Davidson	Tennessee	1,318	1994	36
York	South Carolina	1,295	2025	35
Richland	South Carolina	1,275	2044	34
Pierce	Washington	681	2724	13

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.